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Start Over Author "Yu, Fajun" Publisher jve international ltd.
6 results on '"Yu, Fajun"'

1. Transform-domain sparse representation based classification for machinery vibration signals

Author

Wang Shuanghong, Zhou Fengxing, Fan Fuling, and Yu Fajun

Subjects
Computer science, lcsh:Mechanical engineering and machinery, sparse representation-based classification, transform domain, vibration signal, fault diagnosis, 02 engineering and technology, Fault (power engineering), 01 natural sciences, Domain (software engineering), law.invention, law, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, lcsh:TJ1-1570, General Materials Science, 010301 acoustics, Bearing (mechanical), business.industry, Mechanical Engineering, Bearing vibration, Pattern recognition, Sparse approximation, Class (biology), Vibration, 020201 artificial intelligence & image processing, State (computer science), Artificial intelligence, business
Abstract

The working state of machinery can be reflected by vibration signals. Accurate classification of these vibration signals is helpful for the machinery fault diagnosis. A novel classification method for vibration signals, named Transform Domain Sparse Representation-based Classification (TDSRC), is proposed. The method achieves high classification accuracy by three steps. Firstly, time-domain vibration signals, including training samples and test samples, are transformed to another domain, e.g. frequency-domain, wavelet-domain etc. Then, the transform coefficients of the training samples are combined as a dictionary and the transform coefficients of the test samples are sparsely coded on the dictionary. Finally, the class label of the test samples is identified by their minimal reconstruction errors. Although the proposed method is very similar to the Sparse Representation-based Classification (SRC), experimental results illustrates its performance is far superior to SRC in the classification of vibration signals. These experiments include: frequency-domain classification of bearing vibration data from the Case Western Reserve University (CWRU) Bearing Data Center and wavelet-domain classification of six fault-types gearbox vibration data from our rotating machinery experimental platform.

Published
2018

2. The effect of an orifice plate with different orifice numbers and shapes on the damping characteristics of a dual-chamber air spring

Author

Zeng, Xiaohui, Zhang, Liang, Yu, Fajun, Zhou, Jifu, Zeng, Xiaohui, Zhang, Liang, Yu, Fajun, and Zhou, Jifu

Abstract

A dual-chamber air spring is an important vibration absorber. The shape and number of damping orifices directly influence the damping characteristics of dual-chamber air springs. The study investigates the application of damping characteristics of gas-liquid coupling dual-chamber air springs to mitigate the motion of a marine floating platform. An experimental method is used to analyze the influence of changes in the orifice number and shape on the damping characteristics of dual-chamber air springs given the same opening area. To analyze the effect of various orifice shapes, the study adopts a non-dimensional number to measure the image complexity, namely area/perimeter2 ratio. Additionally, two non-dimensional numbers are considered simultaneously, namely the orifice number and tightness factor, and the influence of changes in the orifice number and orifice position layout on damping characteristics of circular and square orifices is investigated, given the same opening area. The damping cloud map and a formula to calculate the damping for the gas-liquid coupling dual-chamber air springs are obtained by analyzing the experimental data. The proposed formula can be used to quickly calculate the damping coefficients of the dual-chamber air springs when the number and position layout of the orifices are known.

Published
2017

4. Classification of machinery vibration signals based on group sparse representation

Author

Yu, Fajun, Zhou, Fengxing, Yu, Fajun, and Zhou, Fengxing

Abstract

The working condition of mechanical equipment can be reflected by vibration signals collected from it. Accurate classification of these vibration signals is helpful for the machinery fault diagnosis. In recent years, the L1-norm regularization based sparse representation for classification (SRC) has obtained huge success in image recognition, especially in face recognition. However, the investigation of SRC for machinery vibration signals shows that the accuracy and sparsity concentration index are not high enough. In this paper, a new classification method for machinery vibration signals is proposed, in which the L1L2-norm regularization based sparse representation, i.e. group sparse representation, is recommended as a coding strategy. The method achieves its idea classification performance by three steps. Firstly, time-domain vibration signals, including training and test samples, are transformed to frequency-domain to reduce the influence of corrupting noise. Then, the transform coefficient vectors of the test samples are coded with a combination of L1-norm and L2-norm constrain on a dictionary, which is constructed by merging the transform coefficient vectors of the training samples. At last, the fault types of the test samples are labeled by identifying their minimal reconstruction errors. The classification results of simulated and experimental vibration signals demonstrate the superiority of proposed method in comparison with the state-of-the-art classifiers.

Published
2016

6. Transform-domain sparse representation based classification for machinery vibration signals.

Author

Yu Fajun, Fan Fuling, Wang Shuanghong, and Zhou Fengxing

Subjects
*FAULT diagnosis, *MACHINERY, *WAVELETS (Mathematics), *ROTATING machinery, *GEARBOXES
Abstract

The working state of machinery can be reflected by vibration signals. Accurate classification of these vibration signals is helpful for the machinery fault diagnosis. A novel classification method for vibration signals, named Transform Domain Sparse Representation-based Classification (TDSRC), is proposed. The method achieves high classification accuracy by three steps. Firstly, time-domain vibration signals, including training samples and test samples, are transformed to another domain, e.g. frequency-domain, wavelet-domain etc. Then, the transform coefficients of the training samples are combined as a dictionary and the transform coefficients of the test samples are sparsely coded on the dictionary. Finally, the class label of the test samples is identified by their minimal reconstruction errors. Although the proposed method is very similar to the Sparse Representation-based Classification (SRC), experimental results illustrates its performance is far superior to SRC in the classification of vibration signals. These experiments include: frequency-domain classification of bearing vibration data from the Case Western Reserve University (CWRU) Bearing Data Center and wavelet-domain classification of six fault-types gearbox vibration data from our rotating machinery experimental platform. [ABSTRACT FROM AUTHOR]

Published
2018
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